Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
This work addresses the problem of data scarcity in Natural Language Understanding (NLU) tasks for practitioners and researchers, offering a new method for text data augmentation.
This paper introduces Data Boost, a text augmentation framework that uses reinforcement learning to guide conditional text generation. It improves classifier performance, especially in low-resource settings, achieving an average F1 improvement of 8.7% across three tasks when using only 10% of the training data.
Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which augments data through reinforcement learning guided conditional generation. We evaluate Data Boost on three diverse text classification tasks under five different classifier architectures. The result shows that Data Boost can boost the performance of classifiers especially in low-resource data scenarios. For instance, Data Boost improves F1 for the three tasks by 8.7% on average when given only 10% of the whole data for training. We also compare Data Boost with six prior text augmentation methods. Through human evaluations (N=178), we confirm that Data Boost augmentation has comparable quality as the original data with respect to readability and class consistency.